Triple

T3968485
Position Surface form Disambiguated ID Type / Status
Subject Wedding Crashers E92271 entity
Predicate mainCharacter P1183 FINISHED
Object John Beckwith E365680 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: John Beckwith | Statement: [Wedding Crashers, mainCharacter, John Beckwith]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Beckwith
Context triple: [Wedding Crashers, mainCharacter, John Beckwith]
  • A. John Beckwith chosen
    John Beckwith is the charming, fast-talking divorce mediator and wedding crasher portrayed by Owen Wilson in the comedy film "Wedding Crashers."
  • B. Charles Bebb
    Charles Bebb was a prominent early 20th-century American architect based in Seattle, known for helping shape the city's skyline through major commercial and landmark buildings.
  • C. William Massey
    William Massey was a prominent early 20th-century New Zealand politician who led the Reform Party and served as the country’s Prime Minister from 1912 to 1925.
  • D. John Whiteaker
    John Whiteaker was an American politician who became the first governor of the U.S. state of Oregon after it achieved statehood.
  • E. John Blatchley
    John Blatchley was a British theatre director and educator best known as a co-founder of the influential Drama Centre London acting school.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69aed96624188190ac8c45bb57ab72b5 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef992d6bc8190be1b244eb87f2964 completed March 9, 2026, 4:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b533c189a88190b3a81c63621b98ac completed March 14, 2026, 10:09 a.m.
Created at: March 9, 2026, 3:32 p.m.